Paleowave

Pubsplained #2: How many forams for a good climate signal?

Citation

Thirumalai, K., J. W. Partin, C. S. Jackson, and T. M. Quinn (2013), Statistical constraints on El Niño Southern Oscillation reconstructions using individual foraminifera: A sensitivity analysis, Paleoceanography, 28(3), 401–412, doi:10.1002/palo.20037. (Free Access!)

Summary

#Pubsplained #2: We provide a method to quantify uncertainty in estimates of past climate variability using foraminifera. This technique uses numerous, individual shells within a sediment sample and analyzes their geochemistry to reconstruct seasonal and year-to-year variations in environmental conditions.

Here is a link to our code.

Pubsplainer

This plot shows how uncertainty in IFA statistics decreases (but not all the way!) as you increase the number of foraminiferal shells analyzed.

This plot shows how uncertainty in IFA statistics decreases (but not all the way!) as you increase the number of foraminiferal shells analyzed.

Planktic foraminifera are tiny, unicellular zooplankton that are widely found in the open ocean and can tolerate a large range of environmental conditions. During their short (2-4 weeks) lifespan, they build shells (or tests) made of calcium carbonate. The tests fall to the seafloor and continually become covered by sediments over time. We can access these foraminiferal tests using sediment-cores and analyze their geochemistry to unravel all sorts of things about past ocean conditions.

Typically, ~10-100 shells of a particular species are taken from a sediment sample, and collectively, analyzed for their isotopic or trace metal composition. This procedure is repeated with each subsequent sample as you move down in the core. Each of these measurements provides an estimate of the "mean climatic state" during the time represented by the sediment sample. In contrast, individual foraminiferal analyses (IFA), i.e. the geochemistry of each shell within a sample, can provide information about month-to-month fluctuations in ocean conditions during that time interval. The statistics of IFA have been used to compare and contrast climate variability between various paleoclimate time periods.

There are many questions regarding the uncertainty and appropriate interpretation of IFA statistics. We addressed some of these issues in this publication. We provided a code that forward-models modern observations of ocean conditions and approximates, with uncertainty, the minimum number of foraminiferal tests required for a skilled reconstruction. In other words: "how many shells are needed for a good climate signal?"

Armed with this algorithm, we tested various cases in the Pacific Ocean to obtain better estimates of past changes in the El Niño/Southern Oscillation, a powerful mode of present-day climate variability. We found that the interpretation of IFA statistics is tightly linked to the study location's climate signal. Namely, we found that the ratio of seasonality1 to interannual variability2 at a site controlled the IFA signal for a given species occurring throughout the year. We then demonstrated that this technique is far more sensitive to changes in El Niño amplitude rather than its frequency.

In the central equatorial Pacific, where the seasonal cycle is minimal and year-to-year changes are strong, we showed that IFA is skillful at reconstructing El Niño. In contrast, the eastern equatorial Pacific surface-ocean is a region where El Niño anomalies are superimposed on a large annual cycle. Here, IFA is better suited to estimate past seasonality and attempting to reconstruct El Niño is problematic. Such a pursuit becomes more complicated due to changes in the past synchrony of El Niño and seasonality.

Our results also suggest that different species of foraminifera, found at different depths in the water column, or with a particular seasonal preference for calcification, might have more skill at recording past changes in El Niño. However, care should be taken in these interpretations too because these preferences (which are biological in nature) might have changed in the past as well (with or without changes in El Niño).

You can use our MATLABTM code, called INFAUNAL, to generate your own probability distributions of the sensitivity of IFA towards seasonality or interannual variability for a given sedimentation rate, number of foraminifera, and climate signal at a core location in the Pacific. Do let me know if you have any difficulties running the code!

1 - The difference in environmental conditions between summer and winter, average over multiple years

2 Changes from year-to-year (could be winter-to-winter or summer-to-summer etc.) within the time period represented by the sediment sample

India's Heat Wave

Melted asphalt on Indian roads during May, 2015 [ Source ]

Melted asphalt on Indian roads during May, 2015 [Source]

Last month, India faced what is purported to be the 5th deadliest heat wave on record for the planet, killing more than 2,300 people! Studies have shown that heat waves and temperature extremes in general will be exacerbated by global warming (see here, here, and here for example). Thus, it would be prudent to anticipate how bad these heat waves will get in the future.

This year is predicted to be a strong El Niño year, comparable to the juggernaut 1997-98 event, when India had its deadliest heat wave till date during May-June 1998. As during the '97-98 Niño event, most of the worst-hit areas were in the southeastern states of Andhra Pradesh and Telangana. Here, as well as throughout most parts of India, May is the hottest month of the year and is hence most conducive for heat waves. However, unlike other parts of India where the southwest monsoons bring rain and respite with its onset during June, southeastern India gets most of its rainfall during the retreating monsoon season (typically during northern hemisphere winter). 

 
Rainfall in the southeastern parts of India mainly derive from the retreating monsoon, peaking during October, while temperatures peak during May, typically when heat waves occur. Thus, summer monsoon rains cannot be depended upon to always alleviate May heat waves in southeastern India.

Rainfall in the southeastern parts of India mainly derive from the retreating monsoon, peaking during October, while temperatures peak during May, typically when heat waves occur. Thus, summer monsoon rains cannot be depended upon to always alleviate May heat waves in southeastern India.

 

In June '97, a full year before the devastating heat wave, the southeastern coast of India was already anomalously warm (see below). May-June '98 was when the full brunt of the heat wave hit. With this being the case, and 2015 being analogous to 1997 June (prior to the Niño), are we to expect a worse heat wave next year?

May-June 1998, during the '97-98 El Niño event, was the deadliest heat wave to hit India

May-June 1998, during the '97-98 El Niño event, was the deadliest heat wave to hit India

In any case, the annual temperature anomalies for the southeastern part of the Indian subcontinent do not look pretty:

Monthly anomalies (black) and annual anomalies (colors).

Monthly anomalies (black) and annual anomalies (colors).

Considering trends in individual months, we see that the summer months of May to August are all warming anomalously, thereby exacerbating already-warm daily temperatures:

The summer months of May-August all seem to be warming anomalously, and will exacerbate already-warm daily temperatures. 

The summer months of May-August all seem to be warming anomalously, and will exacerbate already-warm daily temperatures. 

Take-home message: The Indian heat wave we saw this May is certainly not the worst to come in the coming years. Action and measures to deal with the heat must be taken appropriately. I wouldn't be too surprised if next year brings a scorcher.